Analysis of reported adverse liver reactions associated with drugs used to treat patients with coronavirus disease 2019

Hepatic injury has been documented in patients with coronavirus disease 2019 (COVID-19). However, pharmacotherapy can frequently impact liver alterations, given the known hepatotoxic potential of drugs not effective to treat COVID-19. The objective of the present study was to evaluate reports of suspected liver reactions to drugs used for treating COVID-19, compare their use for other indications among patients with COVID-19, and assess possible interactions between them. We obtained reports on drugs used to treat COVID-19 (tocilizumab, remdesivir, hydroxychloroquine, and/or lopinavir/ritonavir), registered on June 30, 2020, from the Food and Drug Administration Adverse Event Reporting System (FAERS) Public Dashboard. We then analyzed the risk of developing liver events with these drugs by calculating the reported odds ratios (ROR). We identified 662, 744, and 1381 reports related to tocilizumab, lopinavir/ ritonavir, and hydroxychloroquine use, respectively. The RORs (95% confidence intervals) were 6.32 (5.28-7.56), 6.12 (5.22-7.17), and 9.07 (8.00-10.29), respectively, demonstrating an increased risk of liver events among patients with COVID-19 when compared with uninfected patients. The elevated risk of reporting adverse liver events in patients with COVID-19 who receive these drugs, alone or in combination, highlights the need for careful drug selection and efforts to reduce drug combinations without notable benefits. Similar to any other condition, the use of drugs without established efficacy should be avoided

Lessons to be Learnt from Real-World Studies on Immune-Related Adverse Events with Checkpoint Inhibitors: A Clinical Perspective from Pharmacovigilance

The advent of immune checkpoint inhibitors (ICIs) caused a paradigm shift both in drug development and clinical practice; however, by virtue of their mechanism of action, the excessively activated immune system results in a multitude of off-target toxicities, the so-called immune-related adverse events (irAEs), requiring new skills for timely diagnosis and a multidisciplinary approach to successfully manage the patients. In the recent past, a plethora of large-scale pharmacovigilance analyses have characterized various irAEs in terms of spectrum and clinical features in the real world. This review aims to summarize and critically appraise the current landscape of pharmacovigilance studies, thus deriving take-home messages for oncologists. A brief primer to study design, conduction, and data interpretation is also offered. As of February 2020, 30 real-world postmarketing studies have characterized multiple irAEs through international spontaneous reporting systems, namely WHO Vigibase and the US FDA Adverse Event Reporting System. The majority of studies investigated a single irAE and provided new epidemiological evidence about class-specific patterns of irAEs (i.e. anti-cytotoxic T-lymphocyte antigen 4 [CTLA-4] versus anti-programmed cell death 1 [PD-1] receptor, and its ligand [PD-L1]), kinetics of appearance, co-occurrences (overlap) among irAEs, and fatality rate. Oncologists should be aware of both strengths and limitations of these pharmacovigilance analyses, especially in terms of data interpretation. Optimal management (including rechallenge), predictivity of irAEs (as potential biomarkers of effectiveness), and comparative safety of ICIs (also in terms of combination regimens) represent key research priorities for next-generation real-world studies

Toxicities with Immune Checkpoint Inhibitors: Emerging Priorities From Disproportionality Analysis of the FDA Adverse Event Reporting System

Background: Immune checkpoint inhibitors (ICIs), including antibodies targeting cytotoxic T-lymphocyte associated protein 4 (CTLA4) and programmed cell death 1 or its ligand (PD1/PDL1), elicit different immune-related adverse events (irAEs), but their global safety is incompletely characterized. Objective: The aim of this study was to characterize the spectrum, frequency, and clinical features of ICI-related adverse events (AEs) reported to the FDA Adverse Event Reporting System (FAERS). Patients and methods: AEs from FAERS (up to June 2018) recording ICIs (ipilimumab, nivolumab, pembrolizumab, atezolizumab, avelumab, durvalumab) as suspect were extracted. Comprehensive disproportionality analyses were performed through the reporting odds ratio (ROR) with 95% confidence interval (95% CI), using other oncological drugs as comparison. An overview of systematic reviews (OoSRs) was also undertaken to identify irAEs with consistent positive associations. Results: ICIs were recorded in 47,266 reports, submitted mainly by consumers receiving monotherapy with anti-PD1/PDL1 drugs. Three areas of toxicity emerged from both disproportionality analysis and the OoSRs (32 studies): endocrine (N = 2863; ROR = 6.91; 95% CI 6.60–7.23), hepatobiliary (2632; 1.33; 1.28–1.39), and respiratory disorders (7240; 1.04; 1.01–1.06). Different reporting patterns emerged for anti-CTLA4 drugs (e.g., hypophysitis, adrenal insufficiency, hypopituitarism, and prescribed overdose) and anti-PD1/PDL1 agents (e.g., pneumonitis, cholangitis, vanishing bile duct syndrome, tumor pseudoprogression, and inappropriate schedule of drug administration). No increased reporting emerged when comparing combination with monotherapy regimens, but multiple hepatobiliary/endocrine/respiratory irAEs were recorded. Conclusions: This parallel approach through contemporary post-marketing analysis and OoSRs confirmed that ICIs are associated with a multitude of irAEs, with different reporting patterns between anti-CTLA4 and anti-PD1/PDL1 medications. Close clinical monitoring is warranted to early diagnose and timely manage irAEs, especially respiratory, endocrine, and hepatic toxicities, which warrant further characterization; patient- and drug-related risk factors should be assessed through analytical pharmaco-epidemiological studies and prospective multicenter registries

Safety of biologics approved for the treatment of rheumatoid arthritis and other autoimmune diseases: a disproportionality analysis from the FDA Adverse Event Reporting System (FAERS)

Introduction: The molecular and pharmacological complexity of biologic disease-modifying antirheumatic drugs used for the management of rheumatoid arthritis (RA) favors the occurrence of adverse drug reactions (ADRs), which should be constantly monitored in post-marketing safety studies. Objective: The aim of this study was to identify signals of disproportionate reporting (SDR) of clinical relevance related to the use of biologic drugs approved for RA and other autoimmune diseases. Methods: All suspected ADRs registered in the FDA Adverse Event Reporting System between January 2003 and June 2016 were collected. The reporting odds ratio was used as a measure of disproportionality to identify possible SDRs related to biologics. Those involving important medical events and designated medical events (DME) were prioritized. Results: In total, 2602 SDRs were prioritized. The most commonly reported were ‘Infections and infestations’ (32.2%) and ‘Neoplasms benign, malignant, and unspecified’ (20.4%), and were mainly related to use of infliximab (25.3%, p < 0.001, and 28.8%, p = 0.002, respectively). Sixty-three signals involving DMEs were identified, most of which were related to rituximab (n = 27), and were mainly due to ‘blood disorders’. Amongst the DMEs detected for more than one biologic, ‘intestinal perforation’ and ‘pulmonary fibrosis’ were related to most of them. Conclusions: The results of this study highlight possible safety issues associated with biologics, whose relationship should be more thoroughly investigated. Our results contribute to future research on the identification of clinically relevant risks associated with these drugs, and may help contribute to their rational and safe use

Liver Injury with Nintedanib: A Pharmacovigilance-Pharmacokinetic Appraisal

Drug-induced liver injury (DILI) with nintedanib has emerged as an adverse event of special interest in premarketing clinical trials. We characterized DILI with nintedanib in the real world and explored the underlying pharmacological basis. First, we assessed serious hepatic events reported to the Food and Drug Administration's Adverse Event Reporting System by combining the disproportionality approach [reporting odds ratio (ROR) with 95% confidence interval (CI)] with individual case assessment. Demographic and clinical features were inspected (seriousness, onset, discontinuation, dechallenge/rechallenge, concomitant drugs) to implement an ad hoc causality assessment scoring system. Second, we appraised physiochemical and pharmacokinetic parameters possibly predictive of DILI occurrence. Significant disproportionality was found for nintedanib as compared to pirfenidone (N = 91; ROR = 4.77; 95% CI = 3.15-7.39). Asian population, low body weight (59 kg), and rapid DILI onset (13.5 days) emerged as clinical features. Hospitalization and discontinuation were found in a significant proportion of cases (32% and 36%, respectively). In 24% of the cases, at least two potentially hepatotoxic drugs (statins, proton pump inhibitors, antibiotics) were recorded. Causality was at least possible in 92.3% of the cases. High lipophilicity and predicted in silico inhibition of liver transporters emerged as potential pharmacokinetic features supporting the biological plausibility. Although causality cannot be demonstrated, clinicians should consider early monitoring and medication review on a case-by-case basis

Report on methods of safety signal generation in paediatrics from pharmacovigilance databases

This deliverable is based on the need to develop and test methods for safety signal detection in children. Signal detection is the mainstay of detecting safety issues, but so far very few groups have specifically looked at children. We developed reference sets for positive and negative drugevent combinations and vaccine-event combinations by a systematic literature review on all combinations. We retrieved the FDA AERS database, the CDC VAERS database and EUDRAVIGILANCE database. In order to analyse the datasets we had a stepwise approach from extraction of data, cleaning (e.g. mapping MedDRA and ATC codes) and transformation into a a common data model that we defined for the spontaneous reporting databases. A statistical analysis plan was created for the testing of methods and we provided some descriptive analyses of the FAERS data. Next steps will be to complete the analyses

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

INTRODUCTION: Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. OBJECTIVE: This study aimed to identify methods for the early detection of a wide range of ADR signals. METHODS: First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. RESULTS: We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. CONCLUSIONS: ARM of claims data may be effective in the early detection of a wide range of ADR signals

Signal Fusion and Semantic Similarity Evaluation for Social Media Based Adverse Drug Event Detection

Recent advancements in pharmacovigilance tasks have shown the usage of social media as a resource to obtain real-time signals for drug surveillance. Researchers demonstrated a good potential for the detection of Adverse Drug Events (ADEs) using social media much earlier than the traditional reporting systems maintained by official regulatory authorities like the United States Food and Drug Administration (FDA). Existing automated drug surveillance systems have used various types of social media channels and search query logs for monitoring ADE signals.;In this thesis, we address two key performance issues related to automated drug surveillance systems. The first is to improve the ADE signal detection by analyzing signals from multiple social media channels, and the second is usage of semantic similarity to evaluate ADE narratives detected by drug surveillance systems. Most current approaches for detecting ADEs from social media rely on a single channel: forums or microblogs or query logs. In this study we propose a new methodology to fuse signals from different social media channels. We use graphical causal models to discover potentially hidden connections between data channels, and then use such associations to generate signals for ADEs. Further, prior work have not emphasized much on the language of healthcare consumers, which is often casual and informal in expressing health issues on social media. There is a high potential to miss the semantic similarity between ADE terms extracted from social media and terms from formal official narratives when the two sets of terms do not share exact text. Thus, we exhibit the usage of semantic similarity to enhance accuracy of detected ADEs, and evaluated similarity measurement algorithms developed over biomedical vocabularies in ADE surveillance domain. We experimented on a dataset of drugs which had FDA black box warnings with a retrospective analysis spanning years 2008 to 2015. The results show a better detection rate and an improved performance in terms of precision, recall and timeliness using our proposed methods